EC-SleepMamba: An EEG-Guided Cross-Modal Mamba Framework for Sleep Staging

Automatic sleep staging is an important component of quantitative sleep assessment and provides fundamental support for sleep analysis. Despite recent advances, existing multimodal fusion-based approaches face two challenges: (i) The symmetric fusion strategy ignores the asymmetric clinical prior that EEG is the primary modality, while EOG and EMG serve as auxiliary signals, overlooking the inherent discrepancy in their information contributions. (ii) Jointly modeling intra-epoch dynamics with inter-epoch stage transitions across long-duration physiological signals remains computationally demanding. To address these issues, we propose EC-SleepMamba, an EEG-guided cross-modal Mamba framework that leverages modality complementarity and temporal context to improve staging robustness. Specifically, modality-specific dual-scale CNNs are first introduced to extract high-resolution local features. During sequence encoding, an EEG-guided interaction gate is proposed to condition the auxiliary Cross-Mamba branches on EEG-derived context, enabling directional primary-to-auxiliary information regulation during representation encoding. Finally, a global bi-Mamba module is adopted to capture dynamic transition patterns across epochs. Experiments on Sleep-EDF-39 and Sleep-EDF-153 show that EC-SleepMamba outperforms five competing methods and improves the discrimination of highly confusable stages, demonstrating its effectiveness for sleep staging.